Evaluating the effect of targeted strategies as control tools for hypervirulent meningococcal C outbreaks: a case study from Tuscany, Italy, 2015 to 2016

Background Meningococcus (Neisseria meningitidis) is the causative bacteria of invasive meningococcal disease (IMD), a major cause of meningitis and sepsis. In 2015–16, an outbreak caused by serogroup C meningococci (MenC), belonging to the hyperinvasive strain ST-11(cc-11), resulted in 62 IMD cases in the region of Tuscany, Italy. Aim We aimed to estimate the key outbreak parameters and assess the impact of interventions used in the outbreak response. Methods We developed a susceptible-carrier-susceptible individual-based model of MenC transmission, accounting for transmission in households, schools, discos/clubs and the general community, which was informed by detailed data on the 2015–16 outbreak (derived from epidemiological investigations) and on the implemented control measures. Results The outbreak reproduction number (Re) was 1.35 (95% prediction interval: 1.13–1.47) and the IMD probability was 4.6 for every 1,000 new MenC carriage episodes (95% confidence interval: 1.8–12.2). The interventions, i.e. chemoprophylaxis and vaccination of close contacts of IMD cases as well as age-targeted vaccination, were effective in reducing Re and ending the outbreak. Case-based interventions (including ring vaccination) alone would have been insufficient to achieve outbreak control. The definition of age groups to prioritise vaccination had a critical impact on the effectiveness and efficiency of control measures. Conclusions Our findings suggest that there are no effective alternatives to widespread reactive vaccination during outbreaks of highly transmissible MenC strains. Age-targeted campaigns can increase the effectiveness of vaccination campaigns. These results can be instrumental to define effective guidelines for the control of future meningococcal outbreaks caused by hypervirulent strains.


Socio-demographic model of the population of Tuscany
We developed a synthetic population where the 2.9 million residents of the Tuscan provinces involved by the outbreak are co-located in synthetic households and schools according to the observed sociodemographic statistics. The procedure used to generate synthetic households and schools is reported in detail in Fumanelli et al. [S1]. A comparison between model output and key demographic statistics of the Italian population is shown in Supplementary Figure S1.
Supplementary Figure S1. Comparison between population model and data. A. Age structure of the population.
B. Age distribution of individuals living in households of different sizes. C. Size of schools by level of education.
Due to the remarkable proportion (~53%) of IMD cases linked to transmission in discos and dancing clubs during the 2015-2016 MenC outbreak in Tuscany, we consider in the model 270 venues of this kind in the area, with maximum admittance sampled uniformly between 75 and 750 persons (median size 412.5, total admittance about 111,000), according to available data [S2]. At the beginning of each simulation, each individual was assigned a list of possible venues that she/he can possibly visit. The number of favorite discos/clubs was sampled from a Poisson distribution (mean 1.94), which resulted as the best fit to data from epidemiological investigations [S3] (see Supplementary Figure S2). Favorite discos/clubs were assigned to each individual with probability proportional to a venue's maximum admittance. Discos/clubs were assumed to be empty for six days a week and customers were allocated only one day a week (see Supplementary Table S1) [S4].  [S4]. Sample size is the number of questionnaires compiled by study participants of age between 16 and 40 years old.
Supplementary Figure S2. Distribution of the number of discos/clubs attended by IMD cases likely infected in discos/clubs.
On those days, individuals were sampled to attend a disco/club depending on an age-specific probability obtained from the Italian time-use data (Supplementary Table S2) [S4]. When an individual was selected for disco/club attendance, a venue was assigned to her/him by sampling uniformly from the list of favorite discos/clubs. This ensures that the number of disco/club customers are allocated proportionally to their size.
The average attendance to a disco/club was 65% of its capacity, with over-attendance being rare and limited to at most 10% of the disco/club's maximum attendance. In the baseline model, all individuals in a given age group have the same probability to attend a disco/club. This assumption was subject to sensitivity analysis (see Section 3.4). Close contacts assigned to an individual were distributed across the different possible transmission settings. All household members of a given individual were considered close contacts; the remainder was assigned according to age-specific data for Italy from the Polymod contact study [S5] (Supplementary Figure S3). In particular, we considered contacts classified as "work", "transportation" and "other places" in Polymod as general community contacts in our model, and we sampled them uniformly in the population. Contacts classified as "schools" in Polymod were sampled in the school to which the individual belongs (as random contacts if the individual does not belong to a school); school contacts aged 25+ years represent contacts of university students and schoolteachers or professors. Contacts classified as "leisure" in Polymod were chosen randomly among individuals sharing the same favorite discos/clubs; if the age of an individual was outside the age-class 16-40 years, they had a negligible probability of attending discos/clubs and therefore leisure contacts were sampled randomly in the population (community contacts).
Supplementary Figure S3. Distribution of close contacts outside the household by age of the individual, as used in the model (adapted from [S5]).

Model initialization
We assumed that 0.25% of individuals aged 16-25 years (approx. 800 individuals) are carriers at the beginning of the simulation, using data from [S6] on carriage prevalence in Tuscan provinces not affected by the outbreak.
The initial population in the study area was initialized as vaccinated assuming a constant 90.8% coverage at 1 year of age in the last 10 years, 81.9% coverage in individuals aged 11-15 years and 65.9% coverage in individuals aged 15-20 years [S7]. The proportion of effectively protected individuals was adjusted by taking into account the waning of vaccine-induced immunity (exponentially distributed with average 6 years) [S8]. All other individuals were assumed to be susceptible.

Transmission dynamics
Transmission could occur within households (H), schools (S), discos/clubs (D), and in the general community (random transmission R), with rates ! , where subscript ∈ { , , , } indicates the transmission in each of the four considered social settings. At each time step of the simulation (corresponding to 1 day) and for each setting, each susceptible individual has a probability of acquiring carriage equal to 1 − "# ! $ ! /& ! , where ! and ! are respectively the number of infectious individuals and total number of individuals in the specific instance of the social setting attended by the susceptible individual (e.g., the household where the susceptible individual lives in, the school attended by the susceptible individual, and so on) [S9-S12]. Susceptible close contacts have an increased probability of becoming carriers according to a relative risk . A fraction of individuals who acquire carriage will develop IMD within a few days (with rate ), and a fraction of them will die on the same day of IMD development. Carriers who do not develop IMD will naturally lose carriage with rate , or may be decolonized by antibiotic treatment following contact tracing activities. Antibiotic treatment is assumed to provide protection from carriage for days. Individuals may be vaccinated after contact tracing or during the immunization campaign, and in such case they will mount a protective immunity after a delay of TD days, which will wane at rate . See Supplementary Table S4 ( , ) is the probability mass function of a Poisson distribution with rate λ, i.e. the probability of observing k events if these events occur with a known rate λ.
The model likelihood was also multiplied by a coefficient Θ to take into account the plausibility of the model-

Supplementary Material S4 -Sensitivity analyses
In this section, we evaluate the effect of changes in specific model assumptions by recalibrating the unknown transmission parameters and risk of IMD development. Recalibration was performed using the same procedure described in Section 1.5. Supplementary Table S5  In the baseline analysis, we considered close contacts of carriers to be at higher risk of meningococcal acquisition through parameter , to acknowledge the observation of a carriage prevalence increased by 2.71fold in close contacts of IMD cases [S16]. Here, we change this assumption in such a way to consider a completely homogeneous transmission within each setting ( = 1) and, conversely, a scenario where the excess risk of carriage in close contacts is doubled with respect to the baseline ( = 4.42). The model is equally able to fit the data under these assumptions (Supplementary Figure S8), and the estimated transmission dynamics remain robustly similar to the main analysis (Supplementary Figure S9). Fraction of traced disco/club attendees, Contact tracing in discos/clubs was one of the main control interventions adopted in the considered outbreak.

Supplementary
We assumed that a proportion of attendees to the same discos/clubs of IMD cases was traced, and in the baseline analysis we fixed this proportion to 35% to comply with the total number of traced contacts. Because this parameter is highly uncertain, we evaluated model predictions under the assumptions of = 20% and = 50% respectively. Again, the conclusions of the study were substantially unaffected by uncertainties in this parameter (Supplementary Figures S10-S11).

Modeling of disco/club attendees
In the baseline analysis, we assumed for simplicity that all individuals in a given age group are equally likely to attend a disco/club. Here, we consider an opposite assumption, i.e. that some individuals attend discos/clubs every week and all the others never attend discos/clubs (attendance to discos/clubs in practice is probably somewhere in between these two assumptions, with some individuals attending discos/clubs more frequently than others). We implemented the latter assumption by modifying the algorithm described in Section 1.1 as follows.

Supplementary
Individuals who will attend a disco/club are sampled once and for all at the beginning of each simulation, rather than being re-sampled from the general population every week. Their disco/club of attendance is still reassigned every week by sampling uniformly from its list of favorite discos/clubs. Supplementary Figure S12 shows that this new model slightly overestimates, on average, the contribution of discos/clubs to IMD cases.
Nonetheless, conclusions about the estimated values of the reproductive number remain similar to those of the main analysis, although the prevalence of carriage in this model is assumed to be, on average, slightly lower (Supplementary Figure S13).